Adaptive beamforming algorithms for smart antenna systems

Hossain, Syed Akhter; Islam, Mohammad Tariqul; Serikawa, Seiichi

doi:10.1109/iccas.2008.4694679

Cited by 38 publications

(13 citation statements)

References 8 publications

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“…The substrate material used is FR4 with dielectric constant of 4.3 and thickness (height) 1.6 mm. The first step towards this antenna design is to calculate the length and width of the patch using the following equations [2,[6][7][8]. Lf (Length of QWT feed line) 12.06 8.…”

Section: Design Of E-shape Slot Antennamentioning

confidence: 99%

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Kodgirwar¹,

Deosarkar²,

Joshi³

2020

PIER M

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This paper presents the design of an 8-element linear array for Adaptive Antenna applications using the Least Mean Square (LMS) algorithm towards improving the directive gain, beam steering capabilities, half-power beam-width, side-lobe level, and bandwidth of array. A conventional patch antenna is optimized to operate at 3.6 GHz (5G applications) with two symmetrical slots and Quarter Wave Transformer for feeding, and this design is extended up to 8 elements using CST Microwave Studio parameterization. The Return Loss (S 11), Directivity, HPBW, and VSWR of the antenna array are observed for the 2, 4, and 8 elements adaptive arrays. The inter-element spacing for resulting eight-element antenna array geometry is optimized to obtain maximum directive gain. This geometry appears promising in improving the directive gain from 7.6 dBi to 15.1 dBi for a single element to eight elements, respectively. Further, the LMS algorithm is used to compute the optimal complex weights, considering different angles for the desired User (+45 • and −45 •) and Interferer (+20 • and −20 •) during MATLAB simulation, and then these optimal weights are fed to antenna elements using CST for beam steering in a different direction. Maxima in the direction of user and nulls in the direction of interferer are obtained using CST software and found closely matching with MATLAB results.

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Section: Design Of E-shape Slot Antennamentioning

confidence: 99%

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Kodgirwar¹,

Deosarkar²,

Joshi³

2020

PIER M

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“…On the basis of adaptive algorithms, entries of weight vector are adjusted to minimize the error ݁ሺ݊ሻ between the training signal ݀ሺ݊ሻ and the array output ‫ݕ‬ሺ݊ሻ. The output of the beamformer ‫ݕ‬ሺ݊ሻ can be expressed as given in equation (7), [20] ∑ where ሺ݊ሻ is the weight vector with ‫ݓ‬ ሺ݊ሻ a complex weight for ݇th antenna element at time instant ݊, and ሾ. ሿ ு denotes Hermitian (complex conjugate) transpose.…”

Section: Full Adaptive Beam Antennamentioning

confidence: 99%

“…‫ݔ‬ ሺ݊ሻ is the received baseband signal at ݇th antenna element [9], [20]. Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Squares (RLS), and Direct Matrix Inversion (DMI) are examples of non-blind adaptive algorithm, whereas Constant Modulus Algorithm (CMA) and Decision Directed (DD) algorithms are examples of blind adaptive algorithm [9], [19][20]. These beamforming algorithms have their own pros and cons as far as their computational complexity, convergence speed, stability, robustness against implementation errors and other aspects are concerned.…”

Section: Full Adaptive Beam Antennamentioning

confidence: 99%

Advanced Antenna Techniques and High Order Sectorization with Novel Network Tessellation for Enhancing Macro Cell Capacity in DC-HSDPA Network

Sheikh¹,

Lempiainen²,

Ahnlund³

2013

IJWMN

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“…LMS and its variants are used extensively for filter design and antenna beamforming [9][10][11][12] of adaptive antennas. But for beamforming of adaptive smart antennas either accuracy of desired beam direction or convergence of the algorithms is explained.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Adaptive Beamforming Techniques in Smart Antenna Using LMS Algorithm and Its Variants

Senapati

Ghatak

Roy

2015

2015 International Conference on Computational Intelligence and Networks

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This paper presents a comparative study of beamforming techniques using least mean square (LMS) algorithm and its variants, like, normalized least mean square (NLMS) algorithm and sign least mean square (SLMS) algorithm. The accuracy of beam generation toward the direction of arrival (DoA) and null generation toward the interferer, depends on the value of step size parameter used in the algorithm. Beamwidth and side lobe levels (SLL) are also compared for these three algorithms.

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Adaptive beamforming algorithms for smart antenna systems

Cited by 38 publications

References 8 publications

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Design of Adaptive Array With E-Shape Slot Radiator for Smart Antenna System

Advanced Antenna Techniques and High Order Sectorization with Novel Network Tessellation for Enhancing Macro Cell Capacity in DC-HSDPA Network

A Comparative Study of Adaptive Beamforming Techniques in Smart Antenna Using LMS Algorithm and Its Variants

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